Validation of Skeletal Muscle Volume as a Nutritional Assessment in Patients With Gastric or Colorectal Cancer Before Radical Surgery
Bibliographic record
Abstract
BACKGROUND: Recently, some studies have reported the importance of sarcopenia as a prognostic factor in patients with gastrointestinal cancer who have undergone surgery. We aimed to examine skeletal muscle volume for use in nutritional assessment of preoperative patients, and to compare the results with those of other conventional methods of nutritional assessment, such as biochemical or body composition values. METHODS: This was an open cohort study which examined skeletal muscle volume for use in nutritional assessment of preoperative patients. A total of 121 patients with gastrointestinal cancer who underwent radical surgery were enrolled in this study between June 1, 2008 and December 31, 2012. There were 39 and 82 patients with gastric and colorectal cancer, respectively. The primary outcome of this study was postoperative overall survival. The secondary outcomes were postoperative survival from cancer-related deaths, recurrences of cancer after surgery, postoperative complications, and postoperative hospital inpatient stay (measured in days). Univariate and multivariate analyses were used to identify the relevant factors for postoperative outcomes mentioned above. RESULTS: Skeletal muscle volume was a significant (hazard ratio (HR): 3.34, 95% confidence interval (CI): 1.21 - 9.17, P = 0.020) independent prognostic factor for cancer-related deaths in patients with gastric or colorectal cancer who had undergone surgery, and a marginally independent (HR: 2.48, 95% CI: 0.91 - 6.81, P = 0.077) factor that negatively contributed to overall survival in these patients. In contrast, the preoperative skeletal muscle volume was not correlated with the recurrence of cancer, and was not significantly correlated with the occurrence of severe complications after surgery or prolongation of hospitalization. CONCLUSIONS: The preoperative skeletal muscle volume was a significant prognostic factor in patients with gastric or colorectal cancers. Therefore, the estimation of skeletal muscle volume may be important for stable, long-term nutritional assessment in patients with gastrointestinal cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".